2. Physical vs. Behavioral Biometrics Focus of the Study Keystroke Biometrics Impaired/ Distracted Research Experiments Conclusion and Contribution to Biometrics ICB 2015 Dissertation Process

3. METHODEXAMPLESPROPERTIES What you have (P) User IDs, accounts Cards, badges Keys Can be shared Can be duplicated May be Lost or stolen What you know (K) Password, PIN Mother’s maiden name Personal knowledge Many passwords are east to guess Can be shared May be forgotten What you have and what you know (P, K) User ID + Password ATM card + PIN Can be shared PIN is a weak link (Writing the PIN on the card) Something unique about the user (B) Fingerprints Face Iris Voice print Not possible to share Repudiation unlikely Forging is difficult Cannot be lost or stolen TABLE 2.1: EXISTING USER AUTHENTICATION METHODS WITH SOME EXAMPLES OF POSITIVE AND NEGATIVE PROPERTIES

4. TABLE 1.1: THE SIX MOST COMMONLY USED BIOMETRICS (LEFT). SOME OTHER BIOMETRIC IDENTITIES THAT ARE EITHER USED LESS FREQUENTLY OR THAT ARE STILL IN THE EARLY STAGES OF RESEARCH (RIGHT) PHYSIOLOGICALBEHAVIORAL FaceSignature FingerprintVoice Hand geometry Iris PHYSIOLOGICALBEHAVIORAL DNAGait Ear shapeKeystroke OdorLip motion Retina Skin reflectance Thermogram

6. One-handed, typing behavior may not be performed numerous times and may not necessarily be governed by motor control. One handed typing behavior has been found to be erratic when compared to standard two- handed typing behavior, and this study attempts to shed light as to how to better authenticate distracted or impaired typing scenarios.

7. OTHER STUDIES TO FURTHER STRENGTHEN KEYSTROKE BIOMETRICS INCLUDE: (Literature Review)

9. May suffer from Aniridia, absence of an iris Person may be blind Person may have eye tremors Subject may not be able to hear instructions that are needed for a biometric system Speech may be affected due to hearing loss

10. A traditional keystroke biometric system would require the user to always input keystrokes in a normal state. (using both hands) In a normal setting, users may type using one hand only if they are on the phone or drinking a cup of coffee or perhaps one hand or arm is injured.

11. Entered data as part of a simulated quiz The quiz format encouraged users to enter arbitrary long-text input responses Various scenarios were introduced

12. Some of the users could not complete the exam in class and had to use their laptop or desktop at home. The system prompts the users to identify which system they were using to enter their keystrokes.

14. Keystrokes were logged by a JavaScript event logging framework which was embedded into a Moodle learning platform

16. AFTER CAPTURING THE KEYSTROKES FROM USERS THROUGH VARIOUS NORMAL AND IMPAIRED SCENARIOS, WE BEGAN TO RUN SIMULATIONS:

18. RECEIVER OPERATING CHARACTERISTIC CURVES (ROC) ROC CURVE Historically used in signal detection such as RADAR in distinguishing an actual signal from nose Used in Biometrics to plot the FAR and FRR at various operating points (thresholds) EQUAL ERROR RATE (EER) FAR / FRR INTERSECTION The point on the ROC curve where the FAR and FRR are equal The operating point on the ROC curve where the FAR and FRR intersect

19. TABLE 1- FIRST ITERATION RESULTS TRAIN DATATEST DATAFEATURESEER (%) BOTHBothAll3.3 BOTHLeftAll38.04 BOTHRightAll38 Our results were not encouraging with this method as B Train, L and R Test gave us EER’s in the upper 38% range. Typing one handed proved significantly alter a user’s typing behavior and as a result gave us very poor EER rates

20. SECOND EXPERIMENT Next we decided to experiment to determine if one handed typing behavior was so erratic, that it would be difficult to authenticate with the same one handed test sample

21. TABLE 2- SECOND ITERATION RESULTS TRAIN DATATEST DATAFEATURESEER% LEFT LeftAll13.96 RIGHT RightAll15.61 We were pleased to see the results of the single handed train and test data. The EER rates were relatively low, in the mid-teens which concludes that user one handed samples do have a conclusive pattern that can be analyzed and authenticated with a keystroke biometric system with relative efficacy. However, we wanted to try another experiment to determine if we could improve the error rate to a lower number if possible.

22. AFTER CAPTURING THE KEYSTROKES FROM USERS THROUGH VARIOUS NORMAL AND IMPAIRED SCENARIOS, WE BEGAN TO RUN SIMULATIONS: With the intent of lowering EER rates further, we wanted to experiment by filtering features which would better authenticate impaired users. We created the feature sets for left/right sides by filtering the linguistics features to those that contain keys on each side of the keyboard

23. TABLE 3- THIRD ITERATION RESULTS TRAIN DATATEST DATAFEATURESEER% BOTHLeft 35.85 BOTHRight 36.79 LEFTLeft 22.75 RIGHTRight 26.64 Much to our dismay, the left/right feature filter actually worsened the results of our testing. The initial hypothesis was that if a user is typing with one hand, they would perform more natural typing behavior on the segment of the keyboard with the one hand that they were typing with. Our results did not align with this hypothesis and the reason could be related to omission of the segments. OMITTING A SEGMENT of the keyboard excludes many features of the keylogger system which DEGRADED, not improved the results of the experiment.

24. THE GOAL OF A FOURTH ITERATION WAS TO Combine some of the datasets in order to exclude the need for a system to initiate a detector function and then engage various fallback procedures in order to authenticate a user. Therefore, we combined all of the samples into one experiment which included approximately 1200 DATA POINTS PER USER which needed to be split into 5 SAMPLES. The experiment was so large that it required two days to complete.

25. TABLE 4- FOURTH ITERATION RESULTS TRAIN DATATEST DATAFEATURESEER% BOTH-LEFT-RIGHTBoth-left-rightAll12.41 BOTH-LEFT-RIGHTBothAll4.86 BOTH-LEFT-RIGHTLeftAll15.82 BOTH-LEFT-RIGHTRightAll15.74 The results were very encouraging as the EER’s were with the standard margin of error when comparing the training and testing conditions separately. Fewer assumptions are made with this method The method does require that B, L, R samples be collected during the enrollment phase. System doesn’t need to know whether a sample is one-handed when testing. Avoids requiring a detector and fallback procedure for one-handed samples.

26. Each iteration provided valuable information which assisted us in expanding and developing the research Initially, we expected to find patterns between the both hand sample and the one handed sample which could have been identified, isolated and matched accordingly One handed samples were too erratic and could not be matched with decent rates using our tools Keyboard segmentation actually worsened results Combining B+L+R, and testing across all scenarios proved to be the best approach that would authenticate users and provide a seamless test implementation process

27. 01 03 02

28. Strengthening current keystroke biometric systems by including impaired or distracted scenarios Collecting and combining multiple scenarios and testing any scenario against the combined dataset eliminates the need for a fallback procedure and adequately authenticates the user. Gait Facial Recognition (Don’t Smile) Before the contribution, systems did not reasonably account for user variability causing high false reject rates

29. 4 3 1 2 04 02 01 03 A paper on one-handed keystroke biometrics which was based from our research was submitted and accepted to the International Conference on Biometrics (ICB 2015) in Phuket, Thailandpaper on one-handed keystroke We provided our unlabeled dataset and 9 teams from all over the world competed for the top spots Participants competed against each other to obtain the highest classification accuracies and submitted classification results through an online system Competition participants designed classification models trained on the normally-typed samples in an attempt to classify an unlabeled dataset that consists of normally- typed and one-handed samples

32. Dissertation Process Experiences and Best Practices

34. START TO DEFINE THE CHAPTERS FOR THE MANUSCRIPT Intro Problem Statement General Literature Review More focused literature review Experimental Design and Implementation Results Contribution Discussion Future work GENERAL FRAMEWORK) (150+ PAGES, 100+ REFERENCES)

36. YOUR MANUSCRIPT WILL BE VERY LARGE IN SCALE AND CHANGES COULD BE VERY TIME CONSUMING IF YOU DO NOT LEVERAGE THE BUILT IN FEATURES OF WORD Table of Contents Page Numbering Citations References Captions Figures TAKE THE TIME TO SET UP ALL OF THESE FEATURES EARLY IN YOUR MANUSCRIPT

37. WORD 2010 \BIBLIOGRAPHY\STYLE WORD 2013 On most 32-bits machines with Microsoft Word 2010 this will be:  %programfiles%\Microsoft Office\Office14\Bibliography\Style Once the styles are copied to the directory, they will show up every time Microsoft Word is opened. \APPDATA\ROAMING\MICROSOFT\ BIBLIOGRAPHY\STYLE On most machines with Micrososft Word 2013 this will be:  %userprofile%\AppData\Roaming\Microsoft\Bibliography\Style Once the styles are copied to the directory, they will show up every time Microsoft Word is opened.

38. Once the styles are extracted they will populate into the word references tab. Next step would be to open word, select, IEEE- Alphabetical style under references; Install BibWord Extender Populate the manage sources function within the references tab and enter all of the sources for the manuscript; Enter citations, throughout the document, using the insert citation function within references; At the very end, select build references, and they will all appear in alphabetical order. Any changes that occur when revising the documents can be updated with a simple update references click in the references heading.

39. GUIDE FOR FORMATTING THE DISSERTATION DOC BIBWORD STYLES– INCLUDES– IEEE ALPHABETICAL EXTENDER HANDLES NON- BIBWORD STYLES AND ALSO RESOLVES SEVERAL LIMITATIONS OF THE BASIC IMPLEMENTATION BY MICROSOFT.

42. NIELSEN MEDIA RESEARCH 1999-2006ENROLLED INTO THE DPS 2011 QA Coordinator Specialist FAIRLEIGH DICKINSON UNIVERSITY 2006-2012 Coordinator PASSAIC COUNTY COMMUNITY COLLEGE 2012-PRESENT Adjunct Professor STEM Director Executive Assistant to the President Assistant Dean for Academic Affairs Interim Executive Director of Human Resources

43. ABRAHAM LINCOLN